Autonomous vehicles have captured our attention and with good reason, as self-driving cars promise to change our relationship to automobiles and the love affair with cars. The technology needed to bring autonomous vehicles to life is complex. For example, the following market landscape, from Vision Systems Intelligence, includes these components: Processing Sensors Connectivity Mapping Algorithms Security/Safety Development Tools Aside from specific technologies, the unifying principle for any autonomous system, including vehicles, is data. The future automotive data ecosystem will include data from vehicles, sensors built into roads, communication with nearby vehicles, weather, and other sources. This data ecosystem is highly complex and involves multiple parties in both the private sector and in government. As private companies develop technology and algorithms, they must partner with federal, state, and local governments that control the roads and make the policy decisions that will allow autonomous vehicles on the road. It will take years to implement this complex environment completely. The Society of Automotive Engineers created a standard that describes progressive levels [.PDF] for vehicle automation: Given the complexity and importance of the automotive industry and how it is changing, I invited three of the smartest experts in the world together for a discussion on this topic. The all-star discussion took place as episode 240 of the CXOTalk series of conversations with top innovators: Paul Ballew is the Global Chief Data and Analytics Officer for Ford. His group is responsible for Ford’s data strategy, data management, data acquisition, and analytics activities throughout the entire… [Read full story]
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